AI's energy consumption is a growing concern, with AI, data centers, and cryptocurrency consuming nearly 2% of the world's energy in 2022, potentially doubling by 2026. Training an LLM like GPT-3 uses the equivalent energy of 130 homes per year, and AI tasks consume 33 times more energy than task-specific software. MBZUAI's computer science department, led by Xiaosong Ma, is researching energy efficiency in AI hardware to address this problem. Why it matters: As AI adoption accelerates in the GCC, energy-efficient AI hardware and algorithms are critical for sustainable development and reducing carbon emissions in the region.
KAUST researchers led by Dr. Gyorgy Szekely are developing selective porous membranes to replace energy-intensive separation techniques like distillation in the chemical manufacturing industry. These membrane processes could reduce energy consumption by up to 90% compared to traditional methods. Szekely's team uses AI to optimize separation materials by identifying patterns in previously fragmented data. Why it matters: This research has the potential to significantly reduce the environmental impact of chemical manufacturing, a sector known for its high energy consumption.
MBZUAI researchers are developing spiking neural networks (SNNs) to emulate the energy efficiency of the human brain. Traditional deep learning models like those powering ChatGPT consume significant energy, with a single query using 3.96 watts. SNNs aim to mimic biological neurons more closely to reduce energy consumption, as the human brain uses only a fraction of the energy compared to these models. Why it matters: This research could lead to more sustainable and energy-efficient AI technologies, addressing a major challenge in deploying large-scale AI systems.
An MBZUAI team won the Cisco Sustainability Challenge with 'Energy for the People,' an AI-powered solution to improve the national energy grid. The system uses an AI-based rewarding system to motivate energy efficiency among residential energy consumers. The winning team received a six-month mentorship from Cisco experts to develop the project further. Why it matters: The solution addresses the UAE's Energy Strategy 2050 goals to reduce carbon footprint by 70% and increase clean energy consumption by 50% by leveraging AI for sustainable solutions.
KAUST Professor Derya Baran and her team at startup iyris have developed transparent solar panels that can turn windows into a source of renewable energy. The technology allows buildings to generate their own electricity, aligning with Saudi Vision 2030's goals for sustainable energy. iyris' first customer is the Red Sea Farm, another KAUST-based business, which aims to use the windows to improve plant growth and crop yield. Why it matters: This innovation could significantly reduce reliance on fossil fuels and promote sustainable urban development in the region, where cooling demands drive high electricity consumption.
MBZUAI researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
KAUST researchers have achieved a breakthrough by passing the damp-heat test for perovskite solar cells (PSCs), a rigorous assessment of their ability to withstand prolonged exposure to high humidity and temperatures. The team engineered 2D-perovskite passivation layers that block moisture and enhance power conversion efficiencies. The successful test, which requires maintaining 95% of initial performance after 1,000 hours at 85% humidity and 85 degrees Celsius, marks a significant step toward commercialization. Why it matters: This advancement addresses a critical weakness of PSCs and brings the technology closer to competing with silicon solar cells in terms of stability and longevity, crucial for widespread adoption of renewable energy.